Spectral signature interpretation is a spectral analysis technique which relates spectral features produced by a specific system or process to specific physical phenomena of the system or process. The analysis allows an individual to monitor the physical condition of a specific system or process by observing and studying spectral features produced by the system or process. Such systems or processes include both rotating machinery, such as pumps, motors, or turbines, and mechanical structures, including nuclear reactor internals and steam generators.
Prior methods for performing spectral signature interpretation have required the assistance of a specialized analyst. A specialized analyst's approach to spectral signature interpretation is subjective in nature and often results in inaccurate and misleading diagnoses. These, and other, factors have hindered the more widespread application of spectral analysis techniques in industry.
Neutron noise analysis is a useful tool for monitoring safety significant phenomena in nuclear power plants, such as excessive fuel vibration within the core, progressive structural degradation of the core barrel and thermal shield in a pressurized water reactor, and interference of the stability margin in a boiling water reactor. Noise analysis techniques are especially attractive in that existing instrumentation can be used without disturbing normal plant operation.
The use of an expert for data analysis is required to properly monitor safety significant phenomena based upon analysis of the spectral features of neutron noise. Consequently, this existing analysis technique is conditioned upon the subjective interpretation of the analyst, and often produces misleading or inaccurate diagnosis of a nuclear power plant's operating status (i.e., normal or abnormal operating conditions).
The use of artificial intelligence techniques as an aid in the maintenance and operation of nuclear power plant systems has been contemplated, and several applications using expert systems currently exist. J. A. Bernard and Takashi Washio, Expert Systems Applications Within Nuclear Industry, American Nuclear Society, ISBN 0-89448-0340-0. Specifically, application of neural networks as an aid in the maintenance and operation of nuclear power plant systems has been reported.
R. E. Uhrig, Use of Neural Networks in Nuclear Power Plant Diagnostics, Trans. Int. Conf. Availability Improvements Nucl. Power Plants, Madrid, Spain, 10-14 Apr. 1989. Neural computing represents a radical departure from traditional computing methodologies, and has proved useful in pattern recognition, classification, noise filtering, and other applications where traditional computational methods often perform poorly. Alienna J. Maren et al., Handbook of Neural Computing, Theory and Practice, ISBN 0-12-546090-2, Academic Press (1990); Phillip D. Wasserman, Neural Computing, Theory and Practice, ISBN 0-442-20743-3, Van Nostrand Reinhold (1989). Where conventional techniques are comparable to neural techniques, chip implementations of the latter are often preferable if speed is of prime consideration. Also, because of the interpolative nature of neural networks they are suitable for synthesizing complex functions when trained with sample values. The neural network develops an internal representation of the function in the connect weights, allowing fast analysis of unlearned spectra.
Kofi Korsah and Robert E. Uhrig, for example, use an interweaving back propagation network structure to recognize the shift(s) in the position(s) of the resonances in neutron power spectral density (PSD) data. The position of these resonances define the plant signature, and are related to specific causative mechanisms such as fuel vibrations, core barrel motion, and reactivity feedback effects. Investigation of Neural Network Paradigms for the development of Automatic Noise Diagnostic/Reactor Surveillance Systems, Symposium on Nuclear Reactor Surveillance and Diagnostics (SMORN VI), Gatlingburg, Tenn., Vol.2, 60.001-60.11 (1991).
Prior techniques show that the methodology for recognizing abnormalities in plant signatures typically involves reducing the spectral data into a set of descriptors, and observing changes in these descriptors. C. M. Smith, R. C. Gonzalez and K. R. Piety report the use of statistical pattern recognition techniques to reduce the plant PSD into eight descriptors or descriminants. C. M. Smith and R. C. Gonzalez, Long-Term Automated Surveillance of a Commercial Nuclear Power Plant, Prog. Nucl. Energy, 15, 17-26 (1985); K. R. Piety, Statistical Algorithm for Automated Signature Analysis of Power Spectral Density Data, Prog. Nucl. Energy, 1, 781-802 (1977).
Korsah and Uhrig use a binary feature signature based on the back propagation neural network paradigm in combination with two statistical descriptors to describe the plant signature. Korsah and Uhrig, supra. R. T. Wood and R. B. Perez describe the PSD data from a PWR in terms of four descriptors by deriving a feedback dynamics model of the neutron PSD from a low-order physical model made stochastic by the Langevin technique. R. T. Wood and R. B. Perez, Modeling and Analysis of Neutron Noise from an Ex-Core Detector at a Pressurized Water Reactor, Symposium on Nuclear Reactor surveillance and Diagnostic (SMORN VI), Gatlinburg, Tenn., Vol. 1, 18.01-18.14 (1991).
U.S. Pat. No. 5,023,045, to Watanabe et al. discloses a diagnostic method for monitoring plant malfunctions. The method utilizes model calculated results to form neural network training sets, a tuned neural network to perform system diagnostics, and spectral data descriptors as neural network input. However, the invention relies on a form of pattern recognition, where the pattern is comprised of coherence measurements (a spectral descriptor), to perform its diagnostic function. As a result, the output of the neural network is an indicator corresponding to one of a limited number of physical situations modeled for neural network training. Additionally, the neural network is used to perform classification, that is, to categorize the pattern formed from the measured signal descriptors as one of several patterns used in the training of the neural network. Further, the spectral descriptors used by Watanabe et al., coherence, measure only the degree of commonality between sensor signals. Moreover, no form of spectral feature extraction is utilized in the method.
Other applications for the use of neural networks in monitoring systems have been disclosed. For example, R. E. Uhrig describes the application of a neural network to recognize and distinguish between normal and abnormal power plant conditions based on patterns of room signals. That is, the neural network is used for pattern recognition to classify a set of signals as either normal or abnormal. Application of Neural Networks To Monitoring and Decision Making In The Operation Of Nuclear Power Plants, Inter. Neural Network Conf., Jul. 9-13, 1990.
A similar pattern recognition system is disclosed by Zwingelstein et al.. The publication describes the application of a neural network to recognize abnormal steam generator signals based on Lissajous figures. As with the Uhrig system, the neural network performs pattern recognition. Although the publication also mentions the application of neural networks for predicative maintenance of rotating machinery by using spectral signals, no specific details or result are provided. Application of Expert Systems and Neural Networks for Condition Monitoring and Inspection of French Nuclear Power Plants, Proc. 1990 American Power Conference.
Further, West German Patent No. 4,012,278 and Japanese Patent No. 3,154,896 utilize neural networks in diagnostic systems. However, the neural networks are trained by actual data, and do not rely upon data generated by a mathematical model of the system to train the neural network.
The prior art methods discussed above do not provide for a systematic method to facilitate the monitoring of the physical condition of a specific process or system by interpreting the spectral features produced by the process or system, as is accomplished by the subject invention.